# BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model

arXiv: https://arxiv.org/abs/1705.00831 · HTML: https://mlsanity.com/paper/1705.00831/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2017.

## Built on by (most cited first, 14 of 14)

- [Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling](https://mlsanity.com/paper/2112.11256/) (2021)
- [Public Data-Assisted Mirror Descent for Private Model Training](https://mlsanity.com/paper/2112.00193/) (2021)
- [Private Estimation with Public Data](https://mlsanity.com/paper/2208.07984/) (2022)
- [Private Distribution Learning with Public Data: The View from Sample Compression](https://mlsanity.com/paper/2308.06239/) (2023)
- [Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2404.13804/) (2024)
- [Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks](https://mlsanity.com/paper/2410.17431/) (2024)
- [Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free](https://mlsanity.com/paper/2305.09668/) (2023)
- [Mean Estimation Under Heterogeneous Privacy Demands](https://mlsanity.com/paper/2310.13137/) (2023)
- [Federated Learning with Heterogeneous Differential Privacy](https://mlsanity.com/paper/2110.15252/) (2021)
- [Transfer Learning In Differential Privacy's Hybrid-Model](https://mlsanity.com/paper/2201.12018/) (2022)
- [Locally Differentially Private Bayesian Inference](https://mlsanity.com/paper/2110.14426/) (2021)
- [Federated Learning with Reduced Information Leakage and Computation](https://mlsanity.com/paper/2310.06341/) (2023)
- [Momentum Gradient Descent Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2209.14086/) (2022)
- [Amalgam: A Framework for Obfuscated Neural Network Training on the Cloud](https://mlsanity.com/paper/2406.03405/) (2024)

## Abstract (arXiv)

We propose a hybrid model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model and the trusted curator model, respectively. We demonstrate that within this model, it is possible to design a new type of blended algorithm for the task of privately computing the head of a search log. This blended approach provides significant improvements in the utility of obtained data compared to related work while providing users with their desired privacy guarantees. Specifically, on two large search click data sets, comprising 1.75 and 16 GB respectively, our approach attains NDCG values exceeding 95% across a range of privacy budget values.